In some fields, it is useful to model objects in three dimensions. Modeling such objects proves useful in a variety of applications, particularly where one position is related to another position. For example, modeling the relationship between an input and a response in the human body is useful for medical training exercises, diagnoses, performing remote surgery or for other medical applications. Similarly, modeling the relationship among injection wells and production wells in a reservoir is useful for generating production from oil deposits and recognizing the relationship with other geological applications. The foregoing objects are exemplary only, and other fields may likewise find utility in modeling various objects.
In the field of life sciences, data may be compiled for an output, such as blood pressure, as a function of input, such as an external counterpulsation treatment or angina, where the lower limbs are sequentially squeezed by a cuff, often a pneumatic bladder, during the resting phase of the heart to force blood to the heart and increase the heart's output.
In the field of earth sciences, data is compiled for production well output, as a function of injection well input, to aid in increasing production. In such systems, fluid or gas may be injected into a reservoir via an injection well, resulting after some time in an increase in production at the production well. The data stored for each such system may include well position and depth for each well as well as production output response as a function of injection well operation. This relationship data includes a relationship known as a well allocation factor.
Relationship data is often presented in two-dimensional formats, such as a pie chart or a bar chart. Two-dimensional data formats, however, fail to convey other essential data such as, for example, the positions of input and output sources relative to each other and a related object-like a reservoir grid. Two-dimensional formats are particularly inefficient when attempting to gain a global perspective of the object. When the number of related input sources and/or output sources increase, the two-dimensional data format becomes even more awkward to use.
Thus, there is a need for imaging relationship data in a three-dimensional image which includes an image of the positions of input and output sources relative to each other and a related object.